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      <div class="title">prtRvGmm/prtRvGmm</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtRvGmm</span> - Gaussian Mixture Model Random Variable
 
    RV = <span class="helptopic">prtRvGmm</span> creates a <span class="helptopic">prtRvGmm</span> object with empty
    mixingProportions and prtRvMvn components. These parameters can be
    set manually or by calling the MLE method. A <span class="helptopic">prtRvGmm</span> is a mixture
    of multi-variance normal random variables.
 
    RV = <span class="helptopic">prtRvGmm</span>(PROPERTY1, VALUE1,...) creates a <span class="helptopic">prtRvGmm</span>
    object RV with properties as specified by PROPERTY/VALUE pairs.
 
    A <span class="helptopic">prtRvGmm</span> object inherits all properties from the prtRv class.
    In addition, it has the following properties:
 
    nComponents         - A positive integer specifiying the number of
                          MVN components in the mixture.
    covarianceStructure - The covariance structure applied to each of
                          the prtRvMvn objects in the mixture. See prtRvMvn.
    covariancePool      - A logical specifying whether the components
                          should share a common covariance. If set to
                          true the covariance of the components are 
                          set to the weighted average of the maximum
                          likelihood estimate for the covariance 
                          matrices for the components.
    components          - An array of prtRvMvn objects.
    mixingProportions   - A discrete probability vector, representing
                          the probability of each component in the
                          mixture.
 
   A <span class="helptopic">prtRvGmm</span> object inherits all methods from the prtRv class.
   The MLE method can be used to estimate the distribution parameters
   from data.
 
   Examples:
        ds = prtDataGenOldFaithful;      % Load a data set
        rv = <span class="helptopic">prtRvGmm</span>('nComponents',2);  % Specify 2 components
        rv = mle(rv,ds);                 % Compute the ML estimate
        plotPdf(rv);                     % Plot the estimated PDF
        hold on;
        plot(ds);                        % Overlay the original data</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./../prtRv.html">prtRv</a>, <a href="./../prtRvMvn.html">prtRvMvn</a>, <a href="./../prtRvMultinomial.html">prtRvMultinomial</a>, <a href="./../prtRvUniform.html">prtRvUniform</a>,
              <a href="./../prtRvUniformImproper.html">prtRvUniformImproper</a>, <a href="./../prtRvVq.html">prtRvVq</a>
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